scholarly journals Estimation of Global Solar Radiation in Mubi, Nigeria

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Ogbaka D.T ◽  
Bassi H ◽  
Lami D.S ◽  
Tahir M.A
Keyword(s):  
Solar Radiation ◽  
Global Radiation ◽  
Energy System ◽  
Global Solar Radiation ◽  
Solar Irradiation ◽  
Ann Model ◽  
Acceptable Error ◽  
Solar Radiation Intensity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Application of solar energy system requires having knowledge about solar irradiation potential in different locations. This study therefore used artificial neural networks for predicting solar global radiation by using metrological data. There is no report about prediction of solar radiation potential for Mubi by using Artificial Neural Network (ANN) method. It is very encouraging to observe a very fine agreement between the measured and estimated values shown in study. The ANN Model is considered the best relation for estimating the global solar radiation intensity for Mubi region with an acceptable error. The MSE, RMSE, MBE, MABE and MAPE values are 0.930, 0.964, 0.3358 MJm−2day−1, 0.8175 MJm−2day−1 and 19.30%, respectively. The ANN models appear auspicious for estimating the Global Solar Radiation in the locations where there are no solar radiation measurement stations.

scholarly journals Using the artificial neural networks for prediction and validating solar radiation

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Zahraa E. Mohamed
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Horizontal Surface ◽  
The Other ◽  
Rate Coefficients ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Statistical Indicators ◽  
Better Than

AbstractThe main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

Simulation of Solar Radiation Heat Flux Data for Energy Calculation

1996 ◽  
Vol 118 (1) ◽  
pp. 58-63 ◽  
Author(s):  
A. Panek ◽  
Y. Lee ◽  
H. Tanaka
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Solar Irradiation ◽  
Easy Access ◽  
Energy Calculation ◽  
Radiation Heat ◽  
Statistical Parameters ◽  
Solar Radiation Intensity ◽  
Autocorrelation Time ◽  
Level Of Agreement

The global (or total) and diffuse solar irradiation data are not always available in many areas of the world and they have to be estimated using some sort of empirical models. This paper describes how the sequence of hourly irradiation data can be simulated using some statistical parameters of the global solar radiation intensity such as the monthly average and variance of its daily maxima and autocorrelation time constant. The results of this simulation are compared with the measured data for two different locations, Ottawa, Canada and Warsaw, Poland (these two locations are chosen because of easy access to the radiation data). The comparison shows an acceptable level of agreement between the simulated and measured results.

Estimation of Solar Potential for Thailand Using Adaptive Neurofuzzy Inference System Models

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Jirasuwankul Nirudh ◽  
Jiriwibhakorn Somchat
Keyword(s):  
Solar Radiation ◽  
Real Data ◽  
Ground Level ◽  
Energy System ◽  
Global Solar Radiation ◽  
Inference System ◽  
Average Accuracy ◽  
Artificial Neural Network Ann ◽  
Adaptive Neurofuzzy Inference System ◽  
Energy System Planning

This research paper proposes a new method of global solar radiation prediction for Thailand using adaptive neurofuzzy inference system (ANFIS) models. Contrary to mathematical-based modeling approaches, the proposed models are able to estimate the monthly mean of daily global solar radiation at the ground level without using the earth's atmospheric layer model. The proposed technique alternately utilizes the 9-year long recorded spatiotemporal data of solar irradiance from meteorological ground stations in the modeling process. With a limited number of ground stations, it covered six regions of Thailand, ANFIS modeling; testing and restructuring have been performed repetitively; and finally, the best-fit models with minimum mean absolute percentage errors (MAPEs) corresponding to six regions of Thailand are obtained. Moreover, the ANFIS models have been tested comparatively to the measured data and the multilayer feed forward artificial neural network (ANN) models, which has a good agreement to real data for the proposed models, can be met with the average accuracy of 7.07% MAPE. By applying this model as a tool to estimate solar potential, the local government or the business sector can provide basic information, which is useful for solar energy system planning and project development.

scholarly journals Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 209 ◽  
Author(s):  
Gilles Notton ◽  
Cyril Voyant ◽  
Alexis Fouilloy ◽  
Jean Laurent Duchaud ◽  
Marie Laure Nivet
Keyword(s):  
Solar Radiation ◽  
Energy Systems ◽  
Solar Irradiation ◽  
Optimal Management ◽  
Electrical Networks ◽  
Ann Model ◽  
Energy Applications ◽  
The World ◽  
Ann Models ◽  
Artificial Neural Network Ann

In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI.

scholarly journals Prediction of Inelastic Response Spectra Using Artificial Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Edén Bojórquez ◽  
Juan Bojórquez ◽  
Sonia E. Ruiz ◽  
Alfredo Reyes-Salazar
Keyword(s):  
Seismic Response ◽  
Response Spectra ◽  
Earthquake Ground Motion ◽  
Ann Model ◽  
Acceptable Error ◽  
Inelastic Response ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Moment ◽  
Ground Motion Records

Several studies have been oriented to develop methodologies for estimating inelastic response of structures; however, the estimation of inelastic seismic response spectra requires complex analyses, in such a way that traditional methods can hardly get an acceptable error. In this paper, an Artificial Neural Network (ANN) model is presented as an alternative to estimate inelastic response spectra for earthquake ground motion records. The moment magnitude (MW), fault mechanism (FM), Joyner-Boore distance (dJB), shear-wave velocity (Vs30), fundamental period of the structure (T1), and the maximum ductility (μu) were selected as inputs of the ANN model. Fifty earthquake ground motions taken from the NGA database and recorded at sites with different types of soils are used during the training phase of the Feedforward Multilayer Perceptron model. The Backpropagation algorithm was selected to train the network. The ANN results present an acceptable concordance with the real seismic response spectra preserving the spectral shape between the actual and the estimated spectra.

Estimation of Global Solar Radiation Using Artificial Neural Networks in Abu Dhabi City, United Arab Emirates

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Maitha Al-Shamisi ◽  
Ali Assi ◽  
Hassan Hejase
Keyword(s):  
Solar Radiation ◽  
United Arab Emirates ◽  
Meteorological Data ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Abu Dhabi ◽  
Accurate Estimation ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The geographical location (Latitude: 24 deg 28′ N and Longitude: 54 deg 22′ E) of Abu Dhabi city in the United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for the estimation of monthly mean global solar radiation (GSR) on a horizontal surface in Abu Dhabi. The ANN models are presented and implemented on a 16-yr measured meteorological data set for Abu Dhabi comprising the maximum daily temperature, mean daily wind speed, mean daily sunshine hours, and mean daily relative humidity between 1993 and 2008. The meteorological data between 1993 and 2003 are used for training the ANN and data between 2004 and 2008 are used for testing the estimated values. Multilayer perceptron (MLP) and radial basis function (RBF) are used as ANN learning algorithms. The results attest to the capability of ANN techniques and their ability to produce accurate estimation models.

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

scholarly journals Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Karoro Angela ◽  
Ssenyonga Taddeo ◽  
Mubiru James
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Single Parameter ◽  
Solar Irradiation ◽  
Global Solar Irradiation ◽  
Sunshine Hours ◽  
Artificial Neural ◽  
Propagation Network

We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).

scholarly journals Mapping Monthly Average Global Solar Radiation over Iraq Using GIS and Heliosat Model

2016 ◽  
Vol 15 (5) ◽  
pp. 6724-6731
Author(s):  
Hussain Z. Ali ◽  
Ali. M. AL-Salihi ◽  
Ahmed. K. AL-Abodee
Keyword(s):  
Solar Radiation ◽  
Cross Validation ◽  
Global Radiation ◽  
Spherical Model ◽  
Energy System ◽  
Global Solar Radiation ◽  
Kriging Interpolation ◽  
Monthly Average ◽  
Radiation Data ◽  
Simple Kriging

The mapping of global solar radiation is important in designing of solar energy system and renewable energy applications, also the global solar radiation estimation and mapping will facilitate engineers and architect purposes and applications. In present paper measured and estimated global solar radiation data was employed. The estimation of global radiation data can give results with acceptable accuracy to establish solar maps of monthly radiation using Geographic Information Systems (GIS) software. Simple Kriging interpolation was used to derive radiation maps over Iraq.  Different models were employed, namely Spherical, Circular, and Gaussian. Solar radiation data for the years 1985,1990,1995,2000 and 2005 were used for the production of solar radiation maps over Iraq. On average, Iraq receives (5000) kWh/m2 of global solar radiation in year 2005. The highest global solar radiation is estimated at 6790kWh/m2 in September while the lowest is 1660 kWh/m2in December. Cross validation was used to find the best model by comparing the error criteria, namely MPE, RMS, MSPE, RMSS, and ASE. It was found the Spherical model gives best results according to the cross validation error criteria.Mapping Monthly Average Global Solar Radiation over Iraq Using GIS and Heliosat Model

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